Practical Weak Supervision: Doing More with Less Data

by Wee Hyong Tok, Amit Bahree, Senja Filipi

Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build.

190 pages, Paperback

Published November 9, 2021

Reviews

[Russell Jurney · Rating 5 out of 5]

This isn’t right, GoodReads crediting me with the book. I started a book on this topic with this publisher but got covid-19 early in the pandemic, developed long covid and severe chronic fatigue and wasn’t able to complete the book. Other authors took the book completely over. I don’t believe any of my content is still in the book, it’s topic changed to be more specific to weak supervision.